applications and trends in data mining
TRANSCRIPT
October 12, 2006 Data Mining: Concepts and Techniques 1
Applications and Trends in Data Mining
Data mining applications
Data mining system products and research prototypes
Additional themes on data mining
Social impacts of data mining
Trends in data mining
Summary
October 12, 2006 Data Mining: Concepts and Techniques 2
Data Mining Applications
Data mining is a young discipline with wide and diverse applications
There is still a nontrivial gap between general principles of data mining and domain-specific, effective data mining tools for particular applications
Some application domains (covered in this chapter)Biomedical and DNA data analysisFinancial data analysisRetail industryTelecommunication industry
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Biomedical and DNA Data Analysis
DNA sequences: 4 basic building blocks (nucleotides): adenine (A), cytosine (C), guanine (G), and thymine (T). Gene: a sequence of hundreds of individual nucleotides arranged in a particular orderHumans have around 30,000 genesTremendous number of ways that the nucleotides can be ordered and sequenced to form distinct genesSemantic integration of heterogeneous, distributed genome databases
Current: highly distributed, uncontrolled generation and use of a wide variety of DNA dataData cleaning and data integration methods developed in data mining will help
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DNA Analysis: Examples
Similarity search and comparison among DNA sequencesCompare the frequently occurring patterns of each class (e.g., diseased and healthy)Identify gene sequence patterns that play roles in various diseases
Association analysis: identification of co-occurring gene sequencesMost diseases are not triggered by a single gene but by a combination of genes acting togetherAssociation analysis may help determine the kinds of genes that are likely to co-occur together in target samples
Path analysis: linking genes to different disease development stagesDifferent genes may become active at different stages of the diseaseDevelop pharmaceutical interventions that target the different stages separately
Visualization tools and genetic data analysis
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Data Mining for Financial Data Analysis
Financial data collected in banks and financial institutions are often relatively complete, reliable, and of high qualityDesign and construction of data warehouses for multidimensional data analysis and data mining
View the debt and revenue changes by month, by region, by sector, and by other factorsAccess statistical information such as max, min, total, average, trend, etc.
Loan payment prediction/consumer credit policy analysisfeature selection and attribute relevance rankingLoan payment performanceConsumer credit rating
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Financial Data Mining
Classification and clustering of customers for targeted marketing
multidimensional segmentation by nearest-neighbor, classification, decision trees, etc. to identify customer groups or associate a new customer to an appropriate customer group
Detection of money laundering and other financial crimesintegration of from multiple DBs (e.g., bank transactions, federal/state crime history DBs)Tools: data visualization, linkage analysis, classification, clustering tools, outlier analysis, and sequential pattern analysis tools (find unusual access sequences)
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Data Mining for Retail Industry
Retail industry: huge amounts of data on sales, customer shopping history, etc.
Applications of retail data mining
Identify customer buying behaviors
Discover customer shopping patterns and trends
Improve the quality of customer service
Achieve better customer retention and satisfaction
Enhance goods consumption ratios
Design more effective goods transportation and distribution policies
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Data Mining in Retail Industry: Examples
Design and construction of data warehouses based on the benefits of data mining
Multidimensional analysis of sales, customers, products, time, and region
Analysis of the effectiveness of sales campaignsCustomer retention: Analysis of customer loyalty
Use customer loyalty card information to register sequences of purchases of particular customersUse sequential pattern mining to investigate changes in customer consumption or loyaltySuggest adjustments on the pricing and variety of goods
Purchase recommendation and cross-reference of items
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Data Mining for Telecomm. Industry (1)
A rapidly expanding and highly competitive industry and a great demand for data mining
Understand the business involved
Identify telecommunication patterns
Catch fraudulent activities
Make better use of resources
Improve the quality of service
Multidimensional analysis of telecommunication data
Intrinsically multidimensional: calling-time, duration, location of caller, location of callee, type of call, etc.
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Data Mining for Telecomm. Industry (2)
Fraudulent pattern analysis and the identification of unusual patterns
Identify potentially fraudulent users and their atypical usage patterns
Detect attempts to gain fraudulent entry to customer accounts
Discover unusual patterns which may need special attention
Multidimensional association and sequential pattern analysis
Find usage patterns for a set of communication services by customer group, by month, etc.
Promote the sales of specific services
Improve the availability of particular services in a region
Use of visualization tools in telecommunication data analysis
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How to Choose a Data Mining System?
Commercial data mining systems have little in common Different data mining functionality or methodology May even work with completely different kinds of data sets
Need multiple dimensional view in selectionData types: relational, transactional, text, time sequence, spatial?System issues
running on only one or on several operating systems?a client/server architecture?Provide Web-based interfaces and allow XML data as input and/or output?
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How to Choose a Data Mining System? (2)
Data sourcesASCII text files, multiple relational data sourcessupport ODBC connections (OLE DB, JDBC)?
Data mining functions and methodologiesOne vs. multiple data mining functionsOne vs. variety of methods per function
More data mining functions and methods per function provide the user with greater flexibility and analysis power
Coupling with DB and/or data warehouse systemsFour forms of coupling: no coupling, loose coupling, semitight coupling, and tight coupling
Ideally, a data mining system should be tightly coupled with a database system
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How to Choose a Data Mining System? (3)
ScalabilityRow (or database size) scalabilityColumn (or dimension) scalabilityCurse of dimensionality: it is much more challenging to make a system column scalable that row scalable
Visualization tools“A picture is worth a thousand words”Visualization categories: data visualization, mining result visualization, mining process visualization, and visual data mining
Data mining query language and graphical user interfaceEasy-to-use and high-quality graphical user interface Essential for user-guided, highly interactive data mining
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Examples of Data Mining Systems (1)
IBM Intelligent MinerA wide range of data mining algorithmsScalable mining algorithmsToolkits: neural network algorithms, statistical methods, data preparation, and data visualization toolsTight integration with IBM's DB2 relational database system
SAS Enterprise Miner A variety of statistical analysis toolsData warehouse tools and multiple data mining algorithms
Mirosoft SQLServer 2000Integrate DB and OLAP with miningSupport OLEDB for DM standard
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Examples of Data Mining Systems (2)
SGI MineSetMultiple data mining algorithms and advanced statisticsAdvanced visualization tools
Clementine (SPSS)An integrated data mining development environment for end-users and developersMultiple data mining algorithms and visualization tools
DBMiner (DBMiner Technology Inc.)Multiple data mining modules: discovery-driven OLAP analysis, association, classification, and clustering Efficient, association and sequential-pattern mining functions, and visual classification toolMining both relational databases and data warehouses
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Visual Data Mining
Visualization: use of computer graphics to create visual images which aid in the understanding of complex, often massive representations of dataVisual Data Mining: the process of discovering implicit but useful knowledge from large data sets using visualization techniques
Computer Graphics
High Performance Computing
Pattern Recognition
Human Computer Interfaces
Multimedia Systems
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Visualization
Purpose of VisualizationGain insight into an information space by mapping data onto graphical primitivesProvide qualitative overview of large data setsSearch for patterns, trends, structure, irregularities, relationships among data.Help find interesting regions and suitable parametersfor further quantitative analysis.Provide a visual proof of computer representations derived
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Visual Data Mining & Data Visualization
Integration of visualization and data miningdata visualizationdata mining result visualizationdata mining process visualizationinteractive visual data mining
Data visualizationData in a database or data warehouse can be viewed
at different levels of abstractionas different combinations of attributes or dimensions
Data can be presented in various visual forms
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Data Mining Result Visualization
Presentation of the results or knowledge obtained from data mining in visual forms
Examples
Scatter plots and boxplots (obtained from descriptive data mining)
Decision trees
Association rules
Clusters
Outliers
Generalized rules
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Boxplots from Statsoft: Multiple Variable Combinations
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Visualization of Data Mining Results in SAS Enterprise Miner: Scatter Plots
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Visualization of Association Rules in SGI/MineSet 3.0
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Visualization of a Decision Tree in SGI/MineSet 3.0
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Visualization of Cluster Grouping in IBM Intelligent Miner
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Data Mining Process Visualization
Presentation of the various processes of data mining in visual forms so that users can see
Data extraction process
Where the data is extracted
How the data is cleaned, integrated, preprocessed, and mined
Method selected for data mining
Where the results are stored
How they may be viewed
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Visualization of Data Mining Processes by Clementine
Understand variations with visualized data
See your solution discovery process clearly
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Interactive Visual Data Mining
Using visualization tools in the data mining process to help users make smart data mining decisions
Example
Display the data distribution in a set of attributes using colored sectors or columns (depending on whether the whole space is represented by either a circle or a set of columns)
Use the display to which sector should first be selected for classification and where a good split point for this sector may be
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Interactive Visual Mining by Perception-Based Classification (PBC)
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Audio Data Mining
Uses audio signals to indicate the patterns of data or the features of data mining resultsAn interesting alternative to visual miningAn inverse task of mining audio (such as music) databases which is to find patterns from audio dataVisual data mining may disclose interesting patterns using graphical displays, but requires users to concentrate on watching patternsInstead, transform patterns into sound and music and listen to pitches, rhythms, tune, and melody in order to identify anything interesting or unusual
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Scientific and Statistical Data Mining (1)
There are many well-established statistical techniques for data analysis, particularly for numeric data
applied extensively to data from scientific experiments and datafrom economics and the social sciences
Regression
predict the value of a response(dependent) variable from one or more predictor (independent) variables where the variables are numeric
forms of regression: linear, multiple, weighted, polynomial, nonparametric, and robust
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Scientific and Statistical Data Mining (2)
Generalized linear modelsallow a categorical response variable (or some transformation of it) to be related to a set of predictor variables similar to the modeling of a numeric response variable using linear regressioninclude logistic regression and Poisson regression
Mixed-effect modelsFor analyzing grouped data, i.e. data that can be classified
according to one or more grouping variables
Typically describe relationships between a response variable and some covariates in data grouped according to one or more factors
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Scientific and Statistical Data Mining (3)
Regression treesBinary trees used for classification and prediction
Similar to decision trees:Tests are performed at the internal nodes
In a regression tree the mean of the objective attribute is computed and used as the predicted value
Analysis of varianceAnalyze experimental data for two or more populations described by a numeric response variable and one or more categorical variables (factors)
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Scientific and Statistical Data Mining (4)
Factor analysisdetermine which variables are combined to generate a given factore.g., for many psychiatric data, one can indirectly measure other quantities (such as test scores) that reflect the factor of interest
Discriminant analysispredict a categorical response variable, commonly used in social scienceAttempts to determine several discriminant functions (linear combinations of the independent variables) that discriminate among the groups defined by the response variable
http://www.spss.com/datamine/factor.htm
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Scientific and Statistical Data Mining (5)
Time series: many methods such as autoregression, ARIMA (Autoregressive integrated moving-average modeling), long memory time-series modeling
Quality control: displays group summary charts
Survival analysispredicts the
probability that a patient undergoing a medical treatment would survive at least to time t (life span prediction)
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Theoretical Foundations of Data Mining (1)
Data reductionThe basis of data mining is to reduce the data representationTrades accuracy for speed in response
Data compressionThe basis of data mining is to compress the given data by encoding in terms of bits, association rules, decision trees, clusters, etc.
Pattern discoveryThe basis of data mining is to discover patterns occurring in the database, such as associations, classification models, sequential patterns, etc.
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Theoretical Foundations of Data Mining (2)
Probability theoryThe basis of data mining is to discover joint probability distributions of random variables
Microeconomic viewA view of utility: the task of data mining is finding patterns that are interesting only to the extent in that they can be used in the decision-making process of some enterprise
Inductive databasesData mining is the problem of performing inductive logic on databases,The task is to query the data and the theory (i.e., patterns) of the databasePopular among many researchers in database systems
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Data Mining and Intelligent Query Answering
A general framework for the integration of data mining and intelligent query answering
Data query: finds concrete data stored in a database; returns exactly what is being asked
Knowledge query: finds rules, patterns, and other kinds of knowledge in a database
Intelligent (or cooperative) query answering: analyzes the intent of the query and provides generalized, neighborhood or associated information relevant to the query
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Is Data Mining a Hype or Will It Be Persistent?
Data mining is a technology
Technological life cycle
Innovators
Early adopters
Chasm
Early majority
Late majority
Laggards
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Life Cycle of Technology Adoption
Data mining is at Chasm!?Existing data mining systems are too genericNeed business-specific data mining solutions and smooth integration of business logic with data mining functions
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Data Mining: Merely Managers' Business or Everyone's?
Data mining will surely be an important tool for managers’decision making
Bill Gates: “Business @ the speed of thought”The amount of the available data is increasing, and data mining systems will be more affordableMultiple personal uses
Mine your family's medical history to identify genetically-related medical conditions Mine the records of the companies you deal with Mine data on stocks and company performance, etc.
Invisible data miningBuild data mining functions into many intelligent tools
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Social Impacts: Threat to Privacy and Data Security?
Is data mining a threat to privacy and data security?“Big Brother”, “Big Banker”, and “Big Business” are carefully watching youProfiling information is collected every time
credit card, debit card, supermarket loyalty card, or frequent flyer card, or apply for any of the aboveYou surf the Web, rent a video, fill out a contest entry form,You pay for prescription drugs, or present you medical care number when visiting the doctor
Collection of personal data may be beneficial for companies and consumers, there is also potential for misuse
Medical Records, Employee Evaluations, Etc.
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Protect Privacy and Data Security
Fair information practicesInternational guidelines for data privacy protectionCover aspects relating to data collection, purpose, use, quality, openness, individual participation, and accountabilityPurpose specification and use limitationOpenness: Individuals have the right to know what information is collected about them, who has access to the data, and how the data are being used
Develop and use data security-enhancing techniquesBlind signaturesBiometric encryptionAnonymous databases
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Trends in Data Mining (1)
Application explorationdevelopment of application-specific data mining systemInvisible data mining (mining as built-in function)
Scalable data mining methodsConstraint-based mining: use of constraints to guide data mining systems in their search for interesting patterns
Integration of data mining with database systems, data warehouse systems, and Web database systemsInvisible data mining
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Trends in Data Mining (2)
Standardization of data mining languageA standard will facilitate systematic development, improve interoperability, and promote the education and use of data mining systems in industry and society
Visual data miningNew methods for mining complex types of data
More research is required towards the integration of data mining methods with existing data analysis techniques for the complex types of data
Web miningPrivacy protection and information security in data mining